Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks

Abstract

Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 ± 14.3% and with a volumetric error of 1.0 ± 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p< 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed.

Keywords

• cardiac magnetic resonance
• late gadolinium enhancement
• scar segmentation
• deep learning